A Multipathway Phosphopeptide Standard for Rapid Phosphoproteomics Assay Development

Recent advances in methodology have made phosphopeptide analysis a tractable problem for many proteomics researchers. There are now a wide variety of robust and accessible enrichment strategies to generate phosphoproteomes while free or inexpensive software tools for quantitation and site localization have simplified phosphoproteome analysis workflow tremendously. As a research group under the Association for Biomolecular Resource Facilities umbrella, the Proteomics Standards Research Group has worked to develop a multipathway phosphopeptide standard based on a mixture of heavy-labeled phosphopeptides designed to enable researchers to rapidly develop assays. This mixture contains 131 mass spectrometry vetted phosphopeptides specifically chosen to cover as many known biologically interesting phosphosites as possible from seven different signaling networks: AMPK signaling, death and apoptosis signaling, ErbB signaling, insulin/insulin-like growth factor-1 signaling, mTOR signaling, PI3K/AKT signaling, and stress (p38/SAPK/JNK) signaling. Here, we describe a characterization of this mixture spiked into a HeLa tryptic digest stimulated with both epidermal growth factor and insulin-like growth factor-1 to activate the MAPK and PI3K/AKT/mTOR pathways. We further demonstrate a comparison of phosphoproteomic profiling of HeLa performed independently in five labs using this phosphopeptide mixture with data-independent acquisition. Despite different experimental and instrumentation processes, we found that labs could produce reproducible, harmonized datasets by reporting measurements as ratios to the standard, while intensity measurements showed lower consistency between labs even after normalization. Our results suggest that widely available, biologically relevant phosphopeptide standards can act as a quantitative “yardstick” across laboratories and sample preparations enabling experimental designs larger than a single laboratory can perform. Raw data files are publicly available in the MassIVE dataset MSV000090564.

Recent advances in methodology have made phosphopeptide analysis a tractable problem for many proteomics researchers. There are now a wide variety of robust and accessible enrichment strategies to generate phosphoproteomes while free or inexpensive software tools for quantitation and site localization have simplified phosphoproteome analysis workflow tremendously. As a research group under the Association for Biomolecular Resource Facilities umbrella, the Proteomics Standards Research Group has worked to develop a multipathway phosphopeptide standard based on a mixture of heavylabeled phosphopeptides designed to enable researchers to rapidly develop assays. This mixture contains 131 mass spectrometry vetted phosphopeptides specifically chosen to cover as many known biologically interesting phosphosites as possible from seven different signaling networks: AMPK signaling, death and apoptosis signaling, ErbB signaling, insulin/insulin-like growth factor-1 signaling, mTOR signaling, PI3K/AKT signaling, and stress (p38/ SAPK/JNK) signaling. Here, we describe a characterization of this mixture spiked into a HeLa tryptic digest stimulated with both epidermal growth factor and insulin-like growth factor-1 to activate the MAPK and PI3K/AKT/mTOR pathways. We further demonstrate a comparison of phosphoproteomic profiling of HeLa performed independently in five labs using this phosphopeptide mixture with dataindependent acquisition. Despite different experimental and instrumentation processes, we found that labs could produce reproducible, harmonized datasets by reporting measurements as ratios to the standard, while intensity measurements showed lower consistency between labs even after normalization. Our results suggest that widely available, biologically relevant phosphopeptide standards can act as a quantitative "yardstick" across laboratories and sample preparations enabling experimental designs larger than a single laboratory can perform. Raw data files are publicly available in the MassIVE dataset MSV000090564.
Signaling through protein phosphorylation is akin to a molecular switch that regulates a wide variety of cellular activity, including cell metabolism, differentiation, and proliferation. Manipulating phosphorylation is the key to unlocking therapies for a wide variety of diseases (1) and several protein kinases are tractable drug targets (2). While a wide variety of biochemical tools are available for studying protein phosphorylation, including radioactive phosphorus and sitespecific antibodies, mass spectrometry remains a powerful technique for tracking phosphorylation. When coupled with immobilized metal ion affinity chromatography (IMAC) (3,4), metal oxide enrichment (e.g., TiO2) (5), strong cation exchange (6), or antibody enrichment (7)(8)(9)(10)(11), tandem mass spectrometry (MS/MS) for phosphopeptides can globally quantify hundreds or thousands of phosphosites in a single injection. To that end, large-scale mass spectrometric analyses of the human phosphoproteome (12)(13)(14) have revealed hundreds of thousands of phosphorylated residues distributed across over half of the human proteome.
Most phosphoproteomics workflows using mass spectrometry make use of data-dependent acquisition (DDA) (15), where the most intense peptide precursors are selected for MS/MS fragmentation using dynamic exclusion to avoid measuring the same peptide multiple times. The reliance on making measurements based on different signal intensities in each sample results in stochastic sampling producing sparse datasets with many missing quantitative values (16). Systematic techniques, such as parallel reaction monitoring (PRM) (17) and data-independent acquisition (DIA) (18,19) use preprogrammed MS/MS windows to monitor phosphopeptides. With PRM, researchers select specific peptide precursors and retention time windows to sample a limited number of targeted peptides with high fidelity. Conversely, DIA actively multiplexes peptide measurements by cofragmenting peptides within a wide precursor isolation window, resulting in proteome-wide quantification at the cost of signal interference. Due to systematic scanning, DIA has a demonstrated track record of producing quantitative measurements that are highly reproducible in multi-instrument (20) and multisite experiments (21). New methods using gas-phase fractionation (GPF) coupled with DIA (22,23) seek to collect PRM quality data across the entire proteome using multiple injections of the same sample measuring different m/z ranges with small (approximately 2 m/z-wide) windows. Additionally, GPF-DIA can be used on sample pools to generate DIA-specific chromatogram libraries for improving the analysis of other standard DIA injections (24,25) and this approach has been successfully applied to largescale phosphoproteomics experiments (26).
Interpretation of phosphopeptides with PRM and DIA methods remains difficult, in part due to isobaric phosphosites where the same peptide can be phosphorylated at multiple residues (27)(28)(29). Combinatorial phosphopeptide libraries (30) or proteome-scale synthetic peptides (31,32) can help improve data analysis or empirically identify interacting binding partners but are difficult to use as internal standards for quantifying endogenous peptides in unknown samples. Here, synthesized stable isotope labeled (SIL) phosphopeptide standards can be used to indicate endogenous signals specific to each site (33-37) because they share the same chemical properties (and thus the same retention time and fragmentation patterns) but with different ion masses. Additionally, spiked SIL phosphopeptides facilitate interlaboratory comparisons (38) and improve quantification across largescale experiments (39,40). Finally, SIL peptides can act as retention time standards to align spectrum libraries to a dataset (41) and aid in interpreting global phosphopeptide experiments.
Purified SIL phosphopeptides remain both expensive and challenging to produce at scale, rendering full proteome coverage with SIL standards impractical. Some publicly available, large-scale SIL phosphoproteomics standards have attempted to produce a reduced representation of the general phosphorylation state by focusing on monitoring selected highly observed peptides that produce easily measured signals (35,38). In this study, we have developed a standard to monitor kinase signaling state by measuring common "hub and spoke" proteins where activation of these hubs can lead to massive changes in downstream signaling (11,42). For example, AKT has as many as 150 substrates (43,44), yet signal transduction is largely controlled by the phosphorylation at two sites: T308 and S473 (45). Monitoring "sentinel" phosphosites like these, which are closer to a wide variety of biological activity and functions, may produce a more refined interpretation of active signaling pathways than phosphosites in reduced-representation assays that are selected strictly because they are easy to observe (33). In this work, we present this multipathway phosphoproteomics SIL standard to measure key biologically relevant phosphosites in human proteins indicating specific kinase or pathway activity and demonstrate its utility in a multilab comparative analysis of a human-derived sample.

Peptide Synthesis
All phosphopeptides were generated using PEPotec SRM Custom Peptide Libraries Synthesis Service (Thermo Fisher Scientific). Briefly, all peptides were synthesized in a crude format using the latest Fmoc solid-phase technology. Synthesized peptides were cleaved using a standard cleavage cocktail and suspended in 0.1% (volume fraction) TFA in 50% (volume fraction) acetonitrile in water. The molecular weight for each peptide was confirmed using a mass spectrometer equipped with a nanospray source. Heavy isotope-labeled amino acids used for synthesis are R (+10.008 Da), K (+8.014 Da), V (+6.014 Da), and A (+4.007 Da).

HeLa Cell Culture
HeLa cells (American Type Culture Collection, CCL-2) were maintained as a monolayer (100% relative humidity, 95% air, 5% CO 2 by volume) in Eagle's minimum essential medium (Corning 10-010) supplemented with, MEM nonessential amino acids (Corning, 25-025-Cl), penicillin-streptomycin solution (Corning, 30-002-Cl), 1 mmol/L sodium pyruvate (Corning, 25-000-Cl), and 10% fetal bovine serum (volume fraction; Sigma-Aldrich, F2442). Cells were cultured to 80% confluency at which point the growth media was removed and the cells were rinsed with 1× PBS. The PBS was removed and replaced with serum-free media and the cells were incubated for 18 h. Following serum starvation, the media was removed from the cells and replaced with fresh serum-free media containing 100 ng/ml human epidermal growth factor (Cell Signaling Technology, #8916) and 100 ng/ml human insulin-like growth factor 1 (Cell Signaling Technology, #8917) and cells were incubated for 10 min at 37 • C. Following treatment, the media were removed, the cells rinsed with 1× PBS, trypsinized, and centrifuged at 310 g n for 5 min. The cell pellet was rinsed again with 1× PBS and centrifuged at 310 g n for 5 min. The final PBS wash was removed and the resulting pellet was frozen on dry ice ethanol and stored at −80 • C.

Sample Preparation Prior to Distribution
Cell pellets (~4 × 10 8 cells) were resuspended in 21 ml of lysis buffer [5% SDS (gravimetric fraction); 50 mmol/L ammonium bicarbonate; protease and phosphatase inhibitor). The solution was incubated on a nutator at 4 • C for 30 min. In total, 143 mg of protein lysate was recovered. For cysteine reduction, 788 μl of 50 mmol/L (20 mM) DTT was added, and the lysate was incubated at 95 • C for 10 min and cooled down to room temperature. For cysteine alkylation, 788 μl of 100 mmol/L (20 mM) iodoacetamide was added and incubated in the dark for 30 min at room temperature. The sample was centrifuged for 10 min at 13,000 g n in a tabletop centrifuge. The supernatant was transferred to a new tube and 2106 μl of 12% phosphoric acid (volume fraction) was added (final volume fraction 1.2%) to acidify. The entire lysate was split into four 50 ml tubes and 34.75 ml of S-Trap buffer was added to each tube. The samples were mixed by vortexing and incubated at room temperature for 5 min. The samples were distributed into 28 S-Trap Midi columns. The columns were spun at 4000 g n for 30 s until the sample passed through the S-trap column. The sample loading procedure was repeated two more times. The captured proteins were washed by adding 3 ml of S-trap buffer by centrifuging for 30 s at 4000 g n . The wash step was repeated three more times. A total of 350 μl of digestion solution (6200 μl of 100 mmol/L tetraethylammonium bromide (TEAB) added to 3600 μl of 2 mg/ml trypsin, or 0.73 μg/μl trypsin in 63 mM TEAB) was added to the S-traps and centrifuged at 200 g n for 1 min. The S-traps were incubated at 47 • C overnight for trypsin digestion. After digestion, 500 μl of 100 mmol/L TEAB was added to each column and centrifuged at 4000 g n for 1 min (first elution). We added 500 μl of 0.2% formic acid (volume fraction) to each column and centrifuged at 4000 g n for 1 min (second elution). Five hundred microliters of 50% acetonitrile (volume fraction in water), 0.2% formic acid (volume fraction in water) was added and centrifuged at 4000 g n for 1 min (third elution). The empty columns were centrifuged again at 4000 g n for 1 min. All the elutions were combined and dried down with a speed vacuum concentrator. The resulting peptides were further purified with a Waters C18 Sep-Pak (35 cubic centimeters, 10 g) according to the manufacturer's protocol. After resuspending the purified HeLa peptides, the peptide amount was measured as 83.9 mg.

Sample Distribution
HeLa samples were divided into 1 mg aliquots spiked with 2 pmol of the heavy SIL peptide mixture and then lyophilized in Eppendorf tubes. Tubes were mailed at room temperature to five independent labs for phosphopeptide enrichment and MS/MS analysis.

Phosphopeptide Enrichment
Phosphopeptides were enriched independently at each lab site using the PTMScan Phospho-Enrichment IMAC Fe-NTA Magnetic Beads (Cell Signaling Technology, Inc., #20432) according to the manufacturer's recommended protocol. Briefly, aliquots of 20 μl 25% IMAC bead slurry (5 μl packed beads added to 15 μl water) were transferred to two 1.7 ml microcentrifuge tubes. Each aliquot of beads was washed three times with 1 ml IMAC wash buffer [0.1% TFA; volume fraction), 80% acetonitrile (volume fraction in water)]. The 1 mg sample of dried HeLa peptides was resuspended in 1 ml IMAC loading buffer [0.1% TFA (volume fraction), 85% acetonitrile (volume fraction in water)] and 500 μl was transferred to each tube of IMAC beads. Next, 500 μl IMAC loading buffer was added to each tube for 1 ml total volume. Beads were rotated for 30 min at room temperature, the supernatant was removed, and beads were washed three times with 1 ml IMAC wash buffer. Phosphopeptides were eluted from beads two times in 50 μl IMAC elution buffer [50% acetonitrile (volume fraction), 2.5% ammonia (volume fraction)] and acidified with 20% TFA (volume fraction). Eluted phosphopeptides from both tubes were combined, dried in a Speed-Vac, and cleaned up using a single STAGE tip.

Liquid Chromatography
After following this phosphopeptide enrichment protocol, LC-based MS/MS measurements were performed on different platforms at five distinct lab sites using similar measurement settings.
Lab Site A Setup -The resulting phosphopeptide mixture was analyzed using an UltiMate 3000 Nano LC coupled to a Fusion Lumos mass spectrometer (Thermo Fisher Scientific). The sample was loaded onto a PepMap 100 C18 trap column (75 μm id × 2 cm length; 3 μm, 100 Å, C18 resin; Thermo Fisher Scientific) at 3 μl/min for 10 min with 2% acetonitrile (volume fraction) and 0.05% TFA (volume fraction) followed by separation on an Acclaim PepMap RSLC 2 μm C18 column (75 μm id × 25 cm length; Thermo Fisher Scientific) at 40 • C. After loading, peptides were separated along a 120 min two-step gradient of 5% to 27.5% mobile phase B (80% acetonitrile and 0.08% formic acid) over 105 min followed by a ramp to 40% mobile phase B over 15 min. Lastly, the gradient was ramped to 95% mobile phase B over 10 min, and held at 95% mobile phase B for 10 min before returning to 5% mobile phase B, all at a flow rate of 300 nl/min. Mobile phase A is 0.1% formic acid (volume fraction) in water. Data were acquired on the mass spectrometer from 10 min to 150 min.
Lab Site B Setup -The resulting phosphopeptide mixture was analyzed using an Easy-nLC 1200 system (Thermo Fisher Scientific) coupled to a Thermo Scientific Q Exactive HF mass spectrometer (Thermo Fisher Scientific). The sample was loaded onto an EASY-Spray ES902 column (75 μm id × 25 cm length; 2 μm, 100 Å, C18 resin; Thermo Fisher Scientific) with 100% mobile phase A (0.1% formic acid (volume fraction) in LC-MS grade water). After loading, peptides were separated along a 120 min two-step gradient of 3% to 26% mobile phase B (85% acetonitrile, 0.1% formic acid; volume fraction) over 90 min followed by a ramp to 40% mobile phase B over 30 min. Lastly, the gradient was ramped to 100% mobile phase B over 1 min, and held at 100% mobile phase B for 3 min before returning to 95% mobile phase A, all at a flow rate of 300 nl/min. Data were acquired on the mass spectrometer throughout the gradient.
Lab Site C Setup -The resulting phosphopeptide mixture was analyzed using an Easy-nLC I coupled to a Q Exactive mass spectrometer (Thermo Fisher Scientific). The sample was loaded onto a house-packed analytical column (100 μm id × 22 cm length; packed with 3 μm, 120 Å, C18 resin, Bischoff Chromatography) at 28,000 kPa for 20 min with 100% mobile phase A [2.9% acetonitrile (volume fraction) and 0.12% formic acid (volume fraction)]. After loading, peptides were separated along a 125 min two-step gradient of 5% to 30% mobile phase B (100% acetonitrile, 0.15% formic acid; volume fraction) over 120 min followed by a ramp to 40% mobile phase B over 5 min. Lastly, the gradient was ramped to 98% mobile phase B over 3 min, and held at 98% mobile phase B for 2 min before returning to 100% mobile phase A, all at a flow rate of 300 nl/min. Data were acquired on the mass spectrometer throughout the gradient.
Lab Site D Setup -The resulting phosphopeptide mixture was analyzed using an Easy-nLC 1000 coupled to a Fusion mass spectrometer (Thermo Fisher Scientific). The sample was loaded onto a PepMap 100 C18 trap column (75 μm id × 2 cm length; 3 μm, 100 Å, C18 resin; Thermo Fisher Scientific) at 2 μl/min for 15 min with 0.10% formic acid (volume fraction) followed by separation on an Acclaim PepMap RSLC 2 μm C18 EasySpray column (75 μm id × 25 cm length; Thermo Fisher Scientific) with temperature set at 45 • C. After loading, peptides were separated along a 90 min multistep analytical run with mobile phase A being 0.1% formic acid (volume fraction) and mobile phase B being acetonitrile with 0.1% formic acid (volume fraction). The gradient started with 4% B and progressed to 9% B at 28 min, 15% B at 56 min, 24% B at 74 min, 35% B at 79 min, and 95% B at 80 min through the end of the run. The flow rate was maintained at 200 nl/min for the entire analysis and data were acquired for the entire run.
Lab Site E Setup -Digested peptides were reconstituted in 2% acetonitrile (volume fraction), 0.1% TFA (volume fraction) and analyzed on a Thermo Scientific Fusion Lumos Orbitrap Mass Spectrometer in conjunction with an UltiMate 3000 RSLCnano UHPLC and EASY-Spray source operating in positive ionization mode. Peptides were loaded on a Thermo Scientific Acclaim PepMap 100 C18 reversedphase pre-column (DX164199, 100 μm x 20 mm, 100 Å, 5 μm) at 5 μl/min for 6 min before being separated using an EASY-Spray C18 reversed-phase analytical column (ES802, 75 μm x 250 mm, 100 Å, 2 μm) and eluted with an increasing percentage of acetonitrile (0% to 50%; volume fraction) throughout a 180 min gradient at a flow rate of 200 nl/min and heated to 40 • C. Specifically, peptides were separated along a 114 min gradient of 2% to 5% acetonitrile in 0.5 min, then 5% to 50% acetonitrile over 113.5 min. Next, the gradient was ramped to 99% acetonitrile over 1 min and held at 99% acetonitrile for 4 min before returning to 2% acetonitrile, all at a flow rate of 200 nl/min. Data were acquired for the entire run.

Mass Spectrometry
GPF-DIA data acquisition was performed individually at each lab site following the protocols described in Pino et al. (25) Briefly, each lab acquired eight GPF-DIA acquisitions with 4 m/z DIA spectra at 30,000 resolution and 55 ms maximum ion injection time. Thermo QE and QE-HF instruments (lab sites B, C, and the library generation site) were configured to use an automatic gain control (AGC) target of 1 x 10 6 ions and a normalized collision energy (NCE) of 27. Thermo Fusion and Fusion Lumos tribrid instruments (lab sites A, D, and E) were configured to acquire higher-energy collisional dissociation MS/MS using the orbitrap detector with an AGC target of 4 x 10 5 ions and an NCE of 30. For all instruments, windows were configured in a staggered window placement with optimized window boundaries to place window boundaries near "forbidden zones" (46)

Phosphopeptide Library Generation
The SIL phosphopeptides were also measured alone in water without an additional background to generate a library. Phosphopeptides were separated with a Waters NanoAcquity UPLC and emitted into a Thermo Q-Exactive HF mass spectrometer. For each injection, a 90 min separation was performed using a pulled tip 75 μm inner diameter fused silica column, which was created packed with 3 μm ReproSil-Pur C18 beads (Dr Maisch) to 300 mm and a similar 150 μm inner diameter trap column packed to 25 mm. Nine GPF-DIA experiments covering 400 m/z to 1300 m/z in 100 m/z width injections were performed using 500 fmol total phosphopeptides to test the presence of each peptide and identify the best-responding charge state for each peptide. Each injection was acquired with 51 DIA spectra (4 m/z precursor isolation windows at 30,000 resolution, AGC target 1e6, maximum inject time 55 ms, 27 NCE) using the same staggered window pattern as the standard analysis. Two precursor spectra, a wide spectrum (400 m/z to 1600 m/z at 60,000 resolution) and a narrow spectrum matching the range (i.e. 390 m/z to 510 m/z, 490 m/z to 610 m/z, 590 m/z to 710 m/z, 690 m/z to 810 m/z, 790 m/z to 910 m/z, 890 m/z to 1010 m/z, 990 m/z to 1110 m/z, 1090 m/z to 1210 m/z, and 1190 m/z to 1310 m/z) were interspersed every 25 MS/ MS spectra configured with an AGC target of 3e6 and a maximum injection time of 100 ms.
The resulting GPF-DIA datasets were demultiplexed using Proteowizard (version 3.0.18299) using the settings "-simAsSpectra -zlib -64 -mzML -filter "peakPicking true 1-" -filter "demultiplex opti-mization=overlap_only" *.raw" and analyzed using Skyline-daily (version 4.1.1.11903). Peptide chromatograms were manually validated based on fragmentation similarity, retention time alignment, and mass accuracy versus entries in the Phosphopedia library (12). Confidently measured peptides were exported as a BLIB spectrum library file and iRTDB retention time calculator file. While nine GPF-DIA fractions were collected for library generation, we observed that few if any peptides were best observed in the 1200 m/z to 1300 m/z fraction. As a result, this fraction was not measured by the individual lab sites.

Targeted Data Analysis
GPF-DIA data were demultiplexed using Proteowizard using the same settings as above and analyzed using Skyline (21.2.0.425). Skyline was configured to extract precursor, b, b++, y, and y++ ions from ion three to the last ion. Up to 12 library ions were chosen with an ion match tolerance of 0.5 m/z. The method match tolerance was configured to 0.055 m/z and a 10 ppm tolerance was used to extract ions from the mzML files across all matching spectra. Using the phosphopeptide library and retention time calculator above, each peptide was manually integrated to remove fragment ions with interference as well as peptides without sufficient signal in the heavy channel. All manual integrations for all lab sites (A-E) were completed by a single individual at the library generation lab site to maintain consistency. Total MS1 and MS2 peak areas were exported for each peptide in each lab site across all GPF injections. Total peak areas were summed across charge states for reporting.

Peptide Selection and Characterization
General purpose phosphopeptide standards are typically designed with several factors in mind. Along with other groups, over the last two decades, the Proteomics Standards Research Group (sPRG) has generated several phosphopeptide standards (47) that have primarily focused on workflow evaluation. These types of standards enable researchers to test sample preparation workflows and instrument configurations on well-characterized sets of phosphopeptides with a broad range of physicochemical properties.
Phosphopeptide enrichment methods using metal-ion affinity or antibodies are manually intensive tasks where significant errors can be introduced. While robotics can help standardize phosphopeptide enrichment workflows (26), minor changes can have large effects on efficiency, which can be additionally problematic if those changes affect a subpopulation of peptides, for example, only doubly and triply phosphorylated peptides (48). As such, multiple standards now exist to evaluate and normalize for enrichment quality on a sample-by-sample basis.
In this work, we sought to generate a phosphoproteomics standard to speed up assay development for common biological pathways in humans. Many cellular signaling pathways are regulated by the same kinases that phosphorylate "hub and spoke" phosphosites, and here we exploit that to generate a concise collection of sites. Working from sites observed in the Phosphopedia online resource (12), we initially selected 179 human phosphopeptides containing sites with known biological effects in seven different signaling pathways: AMPK signaling, death and apoptosis signaling, ErbB signaling, insulin/IGF-1 signaling, mTOR signaling, PI3K/AKT signaling, and stress (p38/SAPK/JNK) signaling (Fig. 1A).
Of the 179 targets initially selected by bioinformatic analysis, we performed the synthesis of 150 phosphopeptides from 89 proteins, covering 96 serine, 28 threonine, and 36 tyrosine sites of phosphorylation. Beyond desalting, these peptides were unpurified "crude" SIL peptides and in some cases were of poor purity as validated by MS1 signal. While some phosphopeptides have been observed hundreds of times in Phosphopedia, others have only been reported in that library a single time (Fig. 1B). If possible, for each site we selected the most common (by the number of observations) singly phosphorylated peptide (143 total), choosing doubly (6 total) and triply (1 total) phosphorylated peptides only when necessary. These 150 peptides were additionally selected to span a wide range in amino acid length (Fig. 1C) and relative indexed retention time estimates to make their observation easier to schedule in a complex background (Fig. 1D).
We analyzed these 150 heavy-labeled phosphopeptides using a nine-injection GPF-DIA scheme measuring signals between 400 m/z and 1300 m/z in 100 m/z width injections. These measurements were made using staggered 4 m/z windows, effectively achieving 2 m/z precursor isolation after staggered demultiplexing using Proteowizard (49). We integrated these peptides using Skyline (50) to check for appropriate abundance, to identify peptides that potentially resolved poorly chromatographically, and to build a DIA-specific phosphopeptide library. The library-generation lab was kept separate from all five test lab sites in order to more accurately mimic library usage in other labs outside of our working group.
Of the 150 SIL peptides, 128 produced a high signal and resolved well, and 10 produced a low signal but resolved well. From this, we constructed a DIA-specific spectrum library containing 138 peptides, some of which were represented by multiple charge states (233 total spectra). Of the remaining peptides, five either resolved poorly with broad peaks >1 min wide or produced signals that were difficult to distinguish from noise and seven did not produce any observable signals within the 400 m/z to 1300 m/z range. A similar analysis of these peptides in a HeLa background indicated that 122 of 138 peptides continued to resolve well in a background proteome. Of the 16 remaining peptides, eight resolved poorly while eight produced signals indistinguishable from the background.

Multilab Validation and Analysis
We validated our multipathway phosphopeptide standard by monitoring endogenous peptides in a HeLa background.
To this end, we stimulated HeLa cells for 10 min using both EGF and IGF-1 after starvation for 18 h to synchronize cells. While there is significant crosstalk between phosphorylation signaling pathways, IGF-1 generally stimulates the PI3K/AKT/ mTOR pathway, while EGF stimulates the MAPK/ERK pathway. This mixed pathway stimulation allowed us to test a significant fraction of sites covered by the multipathway phosphopeptide standard in a single experiment. As described in Figure 2A, we centrally lysed, reduced alkylated, and digested the resulting cell pellets. We then distributed approximately 1 mg of the resulting peptide samples, adding 2 pmol of the heavy phosphopeptide standards, to five independent labs across the country. At each lab site, we enriched for phosphopeptides using a standardized IMAC protocol using Fe-NTA magnetic beads in the PTMScan IMAC kit from Cell Signaling Technologies. Each lab analyzed these samples using a common LC-MS/MS workflow based on GPF-DIA mass spectrometry methods, where each lab used independent LC-MS/MS instrumentation (Fig. 2B). This method was based on the same approach used for library generation and allowed us to test the lab-to-lab variability in phosphopeptide enrichment and mass spectrometry working from a standardized sample and standardized methods. While this method required more injections than a typical targeted PRM experiment, it allowed us to acquire PRM-quality data while sidestepping a potential source of lab-to-lab variability with retention time scheduling.
We found that the overall number of observed heavy and endogenous peptides was surprisingly similar between lab sites, despite the measurements being performed with different platforms spanning several generations (51) of orbitrap-based mass spectrometers (Fig. 3A). The number of heavy peptides ranged from 121 to 130 (average = 125), while the number of light peptides ranged from 63 to 81 (average 71). We found that heavy peptide measurements were quite consistent between labs, where 134 of 135 total measured heavy peptides (99%) were observed by at least two of the labs, and 127 (94%) were observed by at least four of the labs (supplemental Fig. S1A). In contrast, only 80 of 104 total measured endogenous peptides (77%) were observed by at least two of the labs (supplemental Fig. S1B). By maximizing ion injection times for a sufficient length on each MS/MS window, (in this study, 50 msec to 60 msec per window), the number of detections did not scale with instrument age or model, where the oldest instrument (a Q-Exactive Classic at lab site C) performed similarly to the newest instrument (Fusion Lumos at lab sites A and E). This finding underlines the reproducibility of GPF-DIA quantification with orbitrap instruments as an alternative data acquisition approach to achieve PRM-like precision. Based on this result, we believe that especially with lower complexity samples like those produced by phosphopeptide enrichments, GPF-DIA measurements are more limited by obtaining sufficient numbers of ions (e.g., ion flux) than by instrument sensitivity or resolution. Thus, maximizing ion injection time on a trapping detector such as the Orbitrap mass analyzer can lessen the limitation of older instrumentation. Future work is needed to further investigate this result.
In this experiment, we stimulated both the IGF-1 and EGF pathways to activate the MAPK and PI3K/AKT/mTOR pathways in HeLa. As a result, we expect some but not all of the phosphopeptides to be expressed endogenously. That said, we expect that the relative light/heavy ratios should be consistent across lab sites. With centralized data analysis, any lab-to-lab variation observed in these ratios should be due to differences in the LC-MS method, instrumentation, or phosphopeptide recovery during the lab site-specific IMAC enrichment step. We found that the MS2 quantification ratios observed at each lab site were very precise across two orders of magnitude with a median CV of 21.6% (Fig. 3B), and reasonably well across three orders of magnitude (median CV = 32.8%). We found that light/heavy ratios were highly significantly more consistent between labs than MS1 (median CV = 51.9%, p-value = 2.0e-8) and MS2 total intensities (median CV = 64.8%, p-value = 9.6e-11), even though (a) all measurements were performed on Thermo Orbitraps, (b) total intensity datasets were median normalized to remove global bias, and (c) peak intensities were estimated in the same units (ions per second). Peptide abundance variation reflects not just global instrumentation bias but also variability introduced through sample preparation. Since all five sites used independent phosphopeptide enrichment, peptide-to-peptide differences in enrichment made it difficult to calibrate between sites (or even between sample preparations) without a common reference standard (52). Interestingly, MS2-level intensities, which are typically reported in PRM and DIA experiments, are the least consistent between labs most likely due to lab-specific differences in transition selection as part of data analysis.
Both MS1-and MS2-level light/heavy ratios show a high degree of consistency between labs. Since the heavy phosphopeptide standard was added before the enrichment step, lab-to-lab variability in phosphopeptide enrichment affects the A B FIG. 2. Experimental design. A, a schematic showing key steps in our experimental approach to assess the lab-to-lab variability of the multipathway phosphopeptide standard. Key steps, such as cell culture, digestion, mixing, and data analysis were controlled, while variability from IMAC enrichment and mass spectrometry instrumentation was isolated in each lab site. B, all lab sites were instructed to use the same eight-injection GPF-DIA method to ensure consistency between lab sites. In this approach, eight injections of the same sample were made, spanning 100 m/z ranges. Each injection was performed using staggered windowing to achieve 2 m/z precursor isolation (targeted PRM equivalent isolation). DIA, data-independent acquisition; GPF, gas-phase fractionation; IMAC, immobilized metal ion affinity chromatography; PRM, parallel reaction monitoring. sample and the standard equally. This result suggests that heavy phosphopeptide standards can act as a peptide-bypeptide correction factor between sample preparations and even between labs, ensuring consistent quantification with low CVs. As such, we believe that standards like the one we present here could enable quantitative experiments with large scopes that extend beyond what can be performed in a single laboratory.
In general, light/heavy ratio precision between labs scaled with fold change (Fig. 4). Peptides with median fold changes estimated above 1:100 light/heavy were typically measured consistently at every lab site (94% of measurements had both light and heavy peptides), while peptides below that were based on missing (heavy absent) or 0 (light absent) integration values 39% of the time. All quantitative data are detailed in supplemental Table S2. In this experiment, we performed simultaneous IGF-1 and EGF stimulation in order to increase the possibility of observing endogenous signaling in these pathways and did not perform a stimulated/unstimulated assay. As a result, in this experiment the light/heavy ratios do not indicate which sites were stimulated; the ratios only indicate that the sites are observable in the background of HeLa phosphorylation relative to our heavy spike-in phosphopeptides. That said, many of the intensely observed sites are associated with either the AKT1 pathway (e.g., HSPB1 S82) or the MAPK pathway (e.g., HSPB1 S15).
In addition to MS2 quantification, the GPF-DIA method we used here also collected consistent MS1 spectra that could be used for quantification. While MS1 signals are typically not used for quantification in DIA experiments, precursor integration is commonly used to quantify peptides in DDA experiments. Using the same retention time boundaries with Skyline, we integrated both MS1 and MS2 signals for each detected peptide and computed light/heavy ratios. In general, both integration approaches agreed. Using lab site A as a relative benchmark, we found that MS1level light/heavy ratios showed higher variability between lab sites (Fig. 5A) than MS2-level integrations (Fig. 5B), reflecting the somewhat higher MS1-level CV shown in Figure 3B (median CV = 31.5%). For this comparison, lab site A was chosen as a benchmark because it detected the highest number of endogenous peptides. While it may be possible to use MS1-level data to improve MS2-based quantifications (53), this result shows the significant limitation of relying on MS1-level data alone for quantification, even in lower complexity samples such as phosphopeptide enrichments. This is in part due to the fact that some fragment ions with interference can be removed through transition refinement without negatively affecting the quantitative results, while precursor ions typically cannot be removed in a way that still maintains at least three independent quantitative measurements of the peptide. Total intensities measured in each lab at the MS1-level (Fig. 5C)

A B
FIG. 3. Measurement consistency across labs. A, the total number of observed heavy and endogenous peptides at each lab site. For each peptide, the heavy form must be confidently detected (correct retention time, fragmentation, and mass accuracy) for the corresponding endogenous peptide to be considered "observed", ensuring a light/heavy ratio. Similarly, endogenous peptides were only "observed" if they had light/heavy ratios >1/100 to protect against integrating noise. B, a violin plot showing the CV between measurements across labs for the top 30 peptides (first two orders of magnitude in ratio) using either total intensity or the light/heavy ratio for both MS1 and MS2 data. Black boxes indicate the interquartile range, while the white points indicate the median CV. and MS2-level (Fig. 5D) show increased scatter off the ideal 1:1 line, even after accounting for bias with median normalization between datasets.
The change in mass caused by the heavy amino acids used in the phosphopeptide mixture range from +4 Da (A) to +10 Da (R). These heavy amino acids were intentionally FIG. 4. Peptide quantification accuracy across sites. Box plots show the median and estimated quartiles of log 10 normalized light/heavy ratios for confidently observed heavy peptides at each lab site. Whiskers indicate the full range of values, while gray dots indicate the actual ratios for each site. Individual light intensity values in the pink shaded region (with below 1/100 light/heavy ratio) are considered low confidence and marked as "unobserved" in Figure 3. In addition to the sequence, protein, and site, the number of lab sites that confidently observed each heavy peptide (of five total lab sites) is also indicated. Peptides are sorted by the median light/heavy ratio. placed on the peptide C terminus making it easier to differentiate light and heavy peptides using the C-terminal y-ion series. However, this has important implications for how the mixture can be used because both light and heavy peptides produce the same N-terminal b-ion series ions and care must be taken when monitoring these ions. For example, the peptide representing AKT1 S473 is RPHFPQFpSYSASGTA with one heavy alanine residue. In our study, all five lab sites measured this heavy peptide as +3H, which produces a strong b-ion ladder but few if any y-ions (Fig. 6, A-C). The S473 site is phosphorylated by mTOR downstream of IGF1R and is one of the most frequently observed peptides in Phosphopedia (12), but the endogenous form is not observed in this experiment. However, the interpretation with DIA methods is complicated by the small m/z difference between the light and heavy forms (1.336 m/z) such that both forms of the peptide can fall in the same precursor isolation window. While the 4 m/z staggered window method used by lab sites A, B, C, and E can be demultiplexed into two 2 m/z windows that can separate the light and heavy form, lab site D used 4 m/z normal (unstaggered) windows where the light and heavy peptides are cofragmented in the same window such that the heavy signal can be misinterpreted as coming from the light peptide (Fig. 6D). This example underlines the challenges arising from interpreting SIL peptides collected with windows wider than 2 m/z, either from DIA (54) or wide-window DDA (55) measurements.

Challenges and Opportunities With Measuring Heavy Phosphopeptide Standards
Proteins are frequently phosphorylated at neighboring sites (56,57) where each phosphosite can have different biological functions (58) and must be measured independently to fully characterize the upstream or downstream biology. Peptide-tophosphosite assignment is typically performed using site localization algorithms (59)(60)(61)(62)(63)(64)(65), but it can also be performed using heavy standards. The phosphopeptide mixture in this study contains five sets of positional isomers and several peptides in both singly and doubly phosphorylated states. For example, MAPK3 (also known as ERK1) is activated by phosphorylation at both T202 and Y204 by MEK1 in the MAPK signal transduction pathway (66). After stimulating with EGF for only 10 min, we were able to consistently differentially measure T202 and Y204 across all five lab sites (Fig. 7A) where Y204 monophosphorylation was clearly observed while the T202 monophosphorylated species were not. Although the fragment intensity signals are not directly comparable, this result confirms the precise ordering of phosphorylation events in the ERK1 activation loop where tyrosine phosphorylation precedes threonine phosphorylation (67). Similarly, the JUN T91/T93, AFT2 T69/T71, and RAF1 S289/S296 sites are all thought to be ERK substrates and the inclusion of these positional isomers could act as detailed temporal map of ERK activity. Future work is needed to explore the utility of monitoring these peptides as a panel.
Additionally, the phosphopeptide mixture can help assign positional isomers in challenging peptides. For example, RAF1 is putatively phosphorylated at S289 and S296 by MEK1 (68). The peptide containing these phosphosites has ten serines and one threonine that could be potentially phosphorylated, making consistent site localization based on the observation of site-determining fragment ions nearly impossible. In our study, all of the lab sites observed at least three positional isomers, where the most abundant form with the highest number of assigned fragment ions consistently eluted earlier than either S289 or S296 (Fig. 7B).
The combinatorial nature of phosphorylation acceptor sites in peptides underlines another consideration. A limitation of phosphopeptide standards is that they cannot comprehensively contain every potential positional isomer and that some biologically active phosphosites will not be included. For example, this standard contains singly phosphorylated DIpYETDYYR and doubly phosphorylated DIpYETDpYYR, which map to Y1161 and Y1165 in IGF1R, the two most commonly observed sites in PhosphoSitePlus (13). However, the standard is missing other potential positional isomers of this peptide including T1163 and Y1166, which have both been observed in over 50 publications. One advantage of DIA is that it enables the potential to look for unexpected positional isomers at alternate retention times or m/z ranges (28). Resolving these forms from gas-phase rearrangements (69) without SIL standards remains an open challenge.

CONCLUSIONS
The proteomics standards research group of the Association for Biomolecular Resource Facilities developed and validated a multipurpose, SIL phosphopeptide mixture of biologically meaningful phosphosites. This standard will enable researchers to rapidly prototype mass spectrometry assays for key phosphoproteins. In a single run, researchers can monitor dozens of sites with more FIG. 6. Challenges of measuring endogenous AKT1 S473 with wide DIA windows. The peptide RPHFPQFpSYSASGTA produces several fragment ions (A), producing a long consecutive b-ion ladder from b3 to b7 (B), but few y-type ions, as demonstrated by the library entry spectrum (C). The lack of fragment ions containing the heavy-labeled C-terminal alanine residue (light blue) means that these ions must be quantified either from precursors or by separating the light (578.256 m/z) and heavy (579.592 m/z) forms into different precursor isolation windows. D, staggering 4 m/z windows to achieve 2 m/z isolation, as performed by lab sites A, B, C, and E can separate light and heavy integrations. However, normal 4 m/z windows, as performed by lab site D, cofragment light and heavy peptides such that their b-ion signals cannot be separated. DIA, data-independent acquisition. specificity than Western blots, where both protein isomers with similar sequences and positional isomers with identical sequences can be measured with confidence. The multipathway nature of this standard focusing on measuring hub kinases will have immediate applicability to a wide variety of fields, including cancer, aging, and metabolism. In addition, the standard can be used for quality control, as a phosphopeptide retention time standard, or for technology development with new methods including those outside of mass spectrometry (70). While we demonstrate the utility of the standard using GPF-DIA, the design of this experiment was to mimic PRM-quality MS2 data and DDA-quality MS1 data without needing to schedule peptides or rely on instrument control software to select peptides for measurement.
Over the last 15 years, the sPRG has developed several standards with a wide variety of commercial partners to improve community-wide reproducibility and facilitate proteomics-based team science. The sPRG plays an ongoing role in interfacing between the research community needs and commercial partners. To this end, this multipathway phosphopeptide mixture has been developed into a widely available product by Thermo Fisher Scientific. Some changes exist between that product and the mixture we present here. First, some inconsistent peptides were removed due to stability concerns at −20 • C or synthesis challenges with longer peptides, resulting in a final 131 phosphopeptide standard. These peptides have been purified to >97% followed by peptide content determination by amino acid analysis, which will potentially open the door to a new range of experiments focused on absolute quantification. supplemental Table S3 contains detailed information on the providence of each peptide in the starting assay down to those that were validated in this final list. Finally, we have demonstrated the tractability of crosslaboratory studies using heavy peptide standards and DIA. Despite wide ranges in mass spectrometry instrumentation and background with phosphopeptide enrichments, we found that all labs could produce reproducible, harmonized measurements of challenging phosphosites as long as results were reported as ratios relative to the standard. This result suggests that when researchers report quantitative ratios to this standard, those results should be directly comparable without any additional data harmonization. Leveraging a widely accessible standard, we believe that this work provides a roadmap for future phosphoproteomics studies that extend beyond the capabilities of any individual laboratory and gives direction to how the proteomics community can grow to empower reproducible science.

DATA AVAILABILITY
All raw data is publicly available on MassIVE using dataset identifier MSV000090564 (https://doi.org/10.25345/C54 J0B266). A data key for the file associations as well as parameters broken out by lab site can be found in supplemental Table S1. All chromatographic data is publicly available for visualization and manual validation at Panor-amaWeb using the URL https://panoramaweb.org/Ohio% 20State%20University%20-%20Searle%20Lab/sprg2018% 20multipathway%20phosphopeptide%20standard/projectbegin.view Supplemental data -This article contains supplemental data.
Acknowledgments -This work is supported in part by the National Institutes of Health Grant R01-GM133981. Our team would like to thank Gordana Ivosev and Brett Phinney for their helpful discussions. We would also like to acknowledge Thermo Fisher Scientific for freely providing crude synthesized phosphopeptides, Cell Signaling Technologies for freely providing phosphopeptide enrichment kits, Protifi LLC for freely providing S-Trap columns, and Waters Corporation for freely providing desalting columns necessary for the completion of this work. Certain commercial equipment, instruments, software, or materials are identified in this paper in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by NIST, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.
The commercialized material based on the results herein is now available from Thermo Fisher Scientific is the SureQuant Multipathway Phosphopeptide Standard (100 fmol/μL; item number A51745). This product consists of 131 heavy SIL phosphopeptides at AQUA (absolute quantification) grade >97% with purity confirmed using amino acid analysis.
Funding and additional information -This work is supported in part by the Pelotonia Institute for Immuno-Oncology and National Institutes of Health Grant R01-GM133981 and R35-GM150723 to B. C. S. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.